A multi-temporal hyperspectral target detection experiment: evaluation of military setups

This paper shows three experiments from our HyperGreding'19 campaign that combine multi-temporal hyperspectral data to address several essential questions in target detection. The experiments were conducted over Greding, Germany, using a Headwall VNIR/SWIR co-aligned sensor mounted on a drone with a flight altitude of 80 m. Additionally, high-resolution aerial RGB data, GPS measurements, and reference data from a field spectrometer were recorded to support the hyperspectral data pre-processing and the evaluation process for the individual experiments. The focus of the experiments is the detectability of camouflage materials and camouflaged objects. When the goal is to transfer hyperspectral analysis to a practical setting, the analysis must be robust regarding realistic and changing conditions. The first experiment investigates the SAM and the SAMZID approaches for change detection to demonstrate their usefulness for target detection of moving objects within the recorded scene. The goal is to eliminate unwanted changes like shadow areas. The second experiment evaluates the detection of different camouflage net types over two days. This includes camouflage nets in shadows during one flight and brightly illuminated in another due to varying solar elevation angles during the day. We demonstrate the performance of typical hyperspectral target detection and classification approaches for robust detection under these conditions. Finally, the third experiment aims to detect objects and materials behind the cover of camouflage nets by using a camouflage garage. We show that some materials can be detected using an unmixing approach.